D-7-16 Parzen-Window Based Normalized Mutual Information for Image Registration

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概要

• 論文の詳細を見る

• Normalized mutual information(NMI) is a widely-used robust method to design the cost function in image registration, since it is invariant to the size of the overlapping region. Its calculation is based on the joint histogram of the fixed and transformed moving images. Usually, only a discrete joint histogram is considered in the calculation of NMI. This causes that its derivative does not exist and it is only able to use the non-gradient optimization methods, such as Powell's method, to seek the maximum. Gradient-based optimization methods sometimes are more desirable in image registration, since they can run faster than non-gradient-based methods, especially when the number of the parameters is huge. In this paper, a parzen-window based method is proposed. The parzen-window based method is able to estimate a continuous joint histogram, so the NMI is differentiable. We find a second order kernel with a width of two is the best kernel for the parzen-window based method. The close-form solution for the derivative of NMI is also derived in order to apply the gradient-based methods in the optimization of registration. The regular step gradient descent method is compared with Powell's method for the situation of MR-T1 to MR-T2 rigid registration. Experiments show that the simple gradient-based method decreases computing costs to more than 50% compared with the Powell's method.

• 社団法人電子情報通信学会の論文
• 2007-03-07

著者

• Chen Yen-Wei

  College of Information and Science, Ristumeikan University

• Chen Yen

  College Of Information Science And Engineering Ritsumeikan University

• Morikawa Shigehiro

  Biomedical MR Science Center, Shiga University of Medical Science

• Kurumi Yoshimasa

  Department of Surgery, Shiga University of Medical Science

• Chen Y‐w

  College Of Information Science And Engineering Ritsumeikan University

• Morikawa Shigehiro

  滋賀医科大学 外科

• Morikawa Shigehiro

  Biomedical Mr Science Center Shiga University Of Medical Science

• Kurumi Yoshimasa

  Department Of Surgery Shiga University Of Medical Science

• Xu Rui

  Graduate School of Engeneering and Science, Ritsumeikan University

• Tang Song-Yuan

  Department of Opto-electronic Engineering, Beijing Institute of Technology

• Chen Yen-wei

  College Of Information And Science Ristumeikan University

• Kurumi Yoshimasa

  The Department Of Surgery Shiga University Of Medical Science

• XU Rui

  the Graduate School of Engineering and Science, Ritsumeikan University

• TANG Song-Yuan

  the Department of Opto-electronic Engineering, Beijing Institute of Technology

• MORIKAWA Shigehiro

  the Biomedical MR Science Center, Shiga University of Medical Science

• Morikawa Shigehiro

  The Biomedical Mr Science Center Shiga University Of Medical Science

• Morikawa Shigehiro

  Mr Science Research Center Shiga University Of Medical Science

• Tang Song-yuan

  The Department Of Opto-electronic Engineering Beijing Institute Of Technology

• Xu Rui

  The Graduate School Of Engineering And Science Ritsumeikan University

• Morikawa Shigehiro

  Department Of Surgery Shiga University Of Medical Science

• Kurumi Yoshimasa

  Department of Comprehensive Surgery, Shiga University of Medical Science

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